fragmented landscapes Jelle Treep Ecology and Biodiversity group - - PowerPoint PPT Presentation

β–Ά
fragmented landscapes
SMART_READER_LITE
LIVE PREVIEW

fragmented landscapes Jelle Treep Ecology and Biodiversity group - - PowerPoint PPT Presentation

Mar 28, 2023 147 likes β€’795 views

Optimizing seed dispersal in fragmented landscapes Jelle Treep Ecology and Biodiversity group h.j.treep@uu.nl Background: Master Computational Geo-ecology (UvA) Movement and dispersal Ecology Atmospheric sciences PhD Ecology and

slide-1
SLIDE 1

Optimizing seed dispersal in fragmented landscapes

Jelle Treep Ecology and Biodiversity group h.j.treep@uu.nl

slide-2
SLIDE 2

Background: Master Computational Geo-ecology (UvA) Movement and dispersal Ecology Atmospheric sciences

slide-3
SLIDE 3

β€œFlying, floating or hitching a ride: The dispersal

  • f plants in heterogeneous

landscapes.” PhD Ecology and Biodiversity

slide-4
SLIDE 4

Contents

Part 1: Mechanistic modelling seed dispersal by wind Part 2: Timing of dispersal Part 3: Optimal dispersal

slide-5
SLIDE 5

Seed dispersal

  • Colonisation
  • Gene flow
  • Range shifts
  • Nature restoration
  • Habitat loss and fragmentation
  • Environmental change
slide-6
SLIDE 6

Seed dispersal

  • Habitat loss and fragmentation

1900 1950 2000

slide-7
SLIDE 7

Seed dispersal

  • Nature restoration
slide-8
SLIDE 8

Dispersal kernel

Lippe et al. 2013

slide-9
SLIDE 9

Traits

slide-10
SLIDE 10

Obtaining dispersal kernels

Observations

slide-11
SLIDE 11

Fascinating results!! However, can we extrapolate this kernel to other situations?

  • Wind?
  • Surrounding vegetation?
  • Species traits?

Therefore: mechanistic modelling

Obtaining dispersal kernels

slide-12
SLIDE 12

Mechanistic modelling

  • Simple

complex

Okubo & Levin, 1989

Simple model

𝐲 = 𝒗

𝑰 𝒙𝒕

slide-13
SLIDE 13

CELC Katul and Albertson, 1998

  • Simple

complex

Stochastic turbulence

π’š(𝒖+𝟐) = π’š(𝒖) + 𝒗 + 𝒗, 𝒛(𝒖+𝟐) = 𝒛(𝒖) + π’˜, π’œ(𝒖+𝟐) = π’œ(𝒖) + 𝒙, + 𝑿𝒕

slide-14
SLIDE 14
  • Simple

complex

Stochastic turbulence

slide-15
SLIDE 15
slide-16
SLIDE 16
slide-17
SLIDE 17

Mechanistic modelling

RAFLES Bohrer et al., 2008

  • Simple

complex

Large Eddy Simulations

slide-18
SLIDE 18
  • Question and Scale dependent!!!
  • Time scale and spatial scale
  • Computational limits
  • Simple

complex

Large Eddy Simulations Stochastic turbulence Simple model

Mechanistic modelling

slide-19
SLIDE 19

Discussion

Closer to reality? Uncertainty

slide-20
SLIDE 20

Contents

Part 1: Mechanistic modelling seed dispersal by wind Part 2: Timing of dispersal Part 3: Optimal dispersal

slide-21
SLIDE 21

Adaptive traits

  • Seed release height H
  • Seed terminal velocity Ws
slide-22
SLIDE 22

Timing May shape entire dispersal distribution

slide-23
SLIDE 23

Soons & Bullock, 2008 Maurer, 2013

Wind Thermals

slide-24
SLIDE 24

Timing of seed dispersal in wind-dispersed plant species.

Pazos et al. 2013

Frequency seed abscission Frequency wind

slide-25
SLIDE 25

Timescales of seed abscission

  • Threshold drag force

(milliseconds)

  • Material fatigue

(minutes – hours)

  • Ripening

(hours – days)

slide-26
SLIDE 26

Waiting may be risky

  • Seed predation
  • Rain
slide-27
SLIDE 27

AIM

  • Build a framework to study non-random seed abscission

strategies across timescales

QUESTIONS

  • Which timescales are important in the timing of seed dispersal?
  • What are the consequences of non-random seed release for dispersal

kernels?

slide-28
SLIDE 28

Field study

H i e r a c i u m a u r a n t i a c u m

Leontodon hispidus

Observation time June 10 - October 3 May 26 - October 3 Seed terminal velocity in m s-1 0.3 0.9 Number of seeds per inflorescence 50 77 Number of plants 24 34 Number of observations 2633 6427

slide-29
SLIDE 29

Field study

slide-30
SLIDE 30

Wind Turbulence (x) Turbulence (y) Turbulence (z) Dissipation

Coupled Eulerian-Lagrangian Closure model

(CELC, Katul et al. 1998)

slide-31
SLIDE 31

CELC

Input Wind speeds (0-20 m/s) Random turbulence Output οƒ  Input Dispersal kernels for each wind speed (0-20 m/s) Input KNMI data wind and precipitation

Dynamic Dispersal model

Output Dispersal kernels for different sigmoid functions

KNMI data Wind Precipitation

Seed trajectories

slide-32
SLIDE 32

Dynamic dispersal model

Assumptions

  • Seed production constant
  • Probability of abscission
  • Disturbance
  • 99 percentile dispersal distance

p(a) = 1 / (1 + e (-Ξ±(u-Ξ²)) )

slide-33
SLIDE 33

Dispersal kernels

slide-34
SLIDE 34

Results: 99 percentile dispersal distance

slide-35
SLIDE 35

Results

Without disturbance

slide-36
SLIDE 36

Results

With disturbance

Without disturbance

slide-37
SLIDE 37
  • By non-random seed release, plants may increase the

tail of the dispersal kernel (99-percentile by 40 %)

  • Risks prevent a too strong selection for high wind

speeds

  • Both model and field study indicate strong biased

seed abscission at wind speeds above 5-6 m s-1

Conclusions

slide-38
SLIDE 38

Conclusions

𝐲 = 𝒗

𝑰 𝒙𝒕

  • Mechanistic models are flexible tools to estimate dispersal

kernels

  • However, tail of the dispersal kernel is still underestimated
  • Possibly a better representation of traits may improve

estimations > evolutionary insights?

slide-39
SLIDE 39

Contents

Part 1: Mechanistic modelling seed dispersal by wind Part 2: Timing of dispersal Part 3: Optimal dispersal

slide-40
SLIDE 40

Mechanistic perspective versus evolutionary perspective

how dispersal determines fitness of individuals and populations remains unclear

slide-41
SLIDE 41

What is optimal?

Maximize fitness: Nearest unoccupied location?

  • Competition
  • Density dependent mortality
  • Facilitation
  • Habitat fragmentation

Janzen-Connell hypothesis

slide-42
SLIDE 42

Analogy to movement ecology Dispersal is a search

Humphries et al. 2014

slide-43
SLIDE 43

Random walks LΓ©vy versus Brownian

Viswanathan et al. 1999 π‘ž π‘š = π‘šβˆ’πœˆ

slide-44
SLIDE 44

Random walks LΓ©vy versus Brownian

Viswanathan et al. 1999

Remember: Low ΞΌ < 2 more LDD Intermediate ΞΌ β‰ˆ 2 Levy High ΞΌ β‰₯ 3 less LDD

slide-45
SLIDE 45

LΓ©vy walks optimize the search efficiency in random searches Search efficiency = average distance traveled before finding target

Koelzsch et al. 2015 De Jager et al. 2011 Raichlen et al. 2014

slide-46
SLIDE 46

Finding the nearest unoccupied location?

Reynolds et al. 2012

slide-47
SLIDE 47

Does the comparison between animals and plants hold?

slide-48
SLIDE 48

Aim

  • Identify a null model for optimal seed

dispersal strategies in plants

  • Assess how dispersal strategies may

maximize species survival in landscapes that differ in terms of fragmentation and dynamics

slide-49
SLIDE 49

Landscape

Species 1 Species 2 Unsuitable

slide-50
SLIDE 50

Dispersal

slide-51
SLIDE 51

Semelparous species Each generation all individuals disperse an equal amount of seeds

Species 1 Species 2

slide-52
SLIDE 52

Stochastic colonization of a grid cell based

  • n expected seed arrival of both species

6 4

Species 1 Species 2

slide-53
SLIDE 53

Landscapes

Landscape parameters Patch size 1, 2, 4, 8, 16, 32, 64, 128 Interpatch distance 1, 5, 10, 50, 100, 500, 1000 Patch turnover rate 0, 0.01, 0.05, 0.1, 0.5, 1

slide-54
SLIDE 54

Example run

slide-55
SLIDE 55

Pairwise invasibility plot

Homogeneous landscape

slide-56
SLIDE 56

Pairwise invasibility plot

Patchsize 8 | Interpatch distance 50 | Patch turnover 0.1

slide-57
SLIDE 57

Metrics

Patchsize 8 | Interpatch distance 50 | Patch turnover 0.1

slide-58
SLIDE 58

Patchsize 8 | Patch turnover 0.01

Interpatch distance 100 Interpatch distance 500 Interpatch distance 12 Interpatch distance 50

slide-59
SLIDE 59

Patch turnover 0.01

slide-60
SLIDE 60

Interpatch distance Interpatch distance

Patch turnover 0.1

slide-61
SLIDE 61
  • In homogeneous or random unpredictable

landscapes uniform dispersal is optimal

  • In patchy environments it depends on the interpatch

distance whether seeds need to be dispersed closeby

  • r whether an intermediate (LΓ©vy-like) strategy is

better

  • In dynamic landscapes it is generally better to

increase the tail of the dispersal kernel

Conclusions

slide-62
SLIDE 62
  • When all other plant traits are equal, plants can
  • utcompete other plants by optimizing the shape of

dispersal kernels

  • Optimal dispersal strategy largely depends on landscape
  • Dispersal traits can evolve when landscape parameters

are relatively constant over time

Conclusions

slide-63
SLIDE 63

Contents

Part 1: Modelling seed dispersal by wind Part 2: Optimal dispersal Part 3: Timing

slide-64
SLIDE 64

Questions? h.j.treep@uu.nl